Automatic Speech Recognition
NeMo
Finnish
asr
speech-recognition
canary-v2
kenlm
finnish
Eval Results (legacy)
Instructions to use RASMUS/Finnish-ASR-Canary-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- NeMo
How to use RASMUS/Finnish-ASR-Canary-v2 with NeMo:
import nemo.collections.asr as nemo_asr asr_model = nemo_asr.models.ASRModel.from_pretrained("RASMUS/Finnish-ASR-Canary-v2") transcriptions = asr_model.transcribe(["file.wav"]) - Notebooks
- Google Colab
- Kaggle
| # Copyright (c) 2020-2025, NVIDIA CORPORATION. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import pytest | |
| from nemo.collections.diffusion.data.diffusion_taskencoder import cook_raw_images | |
| class TestTaskEncoder: | |
| def test_cook_raw_images(self): | |
| sample = {"jpg": "original_image_data", "png": "control_image_data", "txt": "raw_text_data"} | |
| processed_sample = cook_raw_images(sample) | |
| assert "images" in processed_sample | |
| assert "hint" in processed_sample | |
| assert "txt" in processed_sample | |
| assert processed_sample["images"] == sample["jpg"] | |
| assert processed_sample["hint"] == sample["png"] | |
| assert processed_sample["txt"] == sample["txt"] | |